Figure 1
From: Deep anomaly detection of seizures with paired stereoelectroencephalography and video recordings
Overview of the workflow for continuous monitoring with video and SEEG and real-time analysis in the epilepsy monitoring unit. Patients with DRE receive continuous monitoring of their intracranial SEEG leads (red) and simultaneous video recording in their hospital beds (blue). A convolutional LSTM autoencoder (CNN + LSTM) was applied to the video recordings to calculate a regularity score for each frame over time. This regularity score time series and the SEEG time series (green sequence, bottom left) were then separately fed into an LSTM network to reconstruct their signals (blue sequence, bottom middle) and calculate a reconstruction error (red sequence, bottom right) which was then subjected to a self-supervised dynamically thresholding method to identify anomalous events in real-time.
